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As mentioned in the last chapter, Python allows the writer of an extension
module to define new types that can be manipulated from Python code, much like
strings and lists in core Python.

This is not hard; the code for all extension types follows a pattern, but there
are some details that you need to understand before you can get started.

Note

The way new types are defined changed dramatically (and for the better) in
Python 2.2. This document documents how to define new types for Python 2.2 and
later. If you need to support older versions of Python, you will need to refer
to older versions of this documentation.

The Python runtime sees all Python objects as variables of type
PyObject*. A PyObject is not a very magnificent object - it
just contains the refcount and a pointer to the object’s “type object”. This is
where the action is; the type object determines which (C) functions get called
when, for instance, an attribute gets looked up on an object or it is multiplied
by another object. These C functions are called “type methods”.

So, if you want to define a new object type, you need to create a new type
object.

This sort of thing can only be explained by example, so here’s a minimal, but
complete, module that defines a new type:

Now that’s quite a bit to take in at once, but hopefully bits will seem familiar
from the last chapter.

The first bit that will be new is:

typedefstruct{PyObject_HEAD}noddy_NoddyObject;

This is what a Noddy object will contain—in this case, nothing more than every
Python object contains, namely a refcount and a pointer to a type object. These
are the fields the PyObject_HEAD macro brings in. The reason for the macro
is to standardize the layout and to enable special debugging fields in debug
builds. Note that there is no semicolon after the PyObject_HEAD macro; one
is included in the macro definition. Be wary of adding one by accident; it’s
easy to do from habit, and your compiler might not complain, but someone else’s
probably will! (On Windows, MSVC is known to call this an error and refuse to
compile the code.)

For contrast, let’s take a look at the corresponding definition for standard
Python integers:

Now if you go and look up the definition of PyTypeObject in
object.h you’ll see that it has many more fields that the definition
above. The remaining fields will be filled with zeros by the C compiler, and
it’s common practice to not specify them explicitly unless you need them.

This is so important that we’re going to pick the top of it apart still
further:

PyVarObject_HEAD_INIT(NULL,0)

This line is a bit of a wart; what we’d like to write is:

PyVarObject_HEAD_INIT(&PyType_Type,0)

as the type of a type object is “type”, but this isn’t strictly conforming C and
some compilers complain. Fortunately, this member will be filled in for us by
PyType_Ready().

"noddy.Noddy",/* tp_name */

The name of our type. This will appear in the default textual representation of
our objects and in some error messages, for example:

Note that the name is a dotted name that includes both the module name and the
name of the type within the module. The module in this case is noddy and
the type is Noddy, so we set the type name to noddy.Noddy.
One side effect of using an undotted name is that the pydoc documentation tool
will not list the new type in the module documentation.

sizeof(noddy_NoddyObject),/* tp_basicsize */

This is so that Python knows how much memory to allocate when you call
PyObject_New().

Note

If you want your type to be subclassable from Python, and your type has the same
tp_basicsize as its base type, you may have problems with multiple
inheritance. A Python subclass of your type will have to list your type first
in its __bases__, or else it will not be able to call your type’s
__new__() method without getting an error. You can avoid this problem by
ensuring that your type has a larger value for tp_basicsize than its
base type does. Most of the time, this will be true anyway, because either your
base type will be object, or else you will be adding data members to
your base type, and therefore increasing its size.

0,/* tp_itemsize */

This has to do with variable length objects like lists and strings. Ignore this
for now.

Skipping a number of type methods that we don’t provide, we set the class flags
to Py_TPFLAGS_DEFAULT.

Py_TPFLAGS_DEFAULT,/* tp_flags */

All types should include this constant in their flags. It enables all of the
members defined by the current version of Python.

Now we get into the type methods, the things that make your objects different
from the others. We aren’t going to implement any of these in this version of
the module. We’ll expand this example later to have more interesting behavior.

For now, all we want to be able to do is to create new Noddy objects.
To enable object creation, we have to provide a tp_new implementation.
In this case, we can just use the default implementation provided by the API
function PyType_GenericNew(). We’d like to just assign this to the
tp_new slot, but we can’t, for portability sake, On some platforms or
compilers, we can’t statically initialize a structure member with a function
defined in another C module, so, instead, we’ll assign the tp_new slot
in the module initialization function just before calling
PyType_Ready():

This method decrements the reference counts of the two Python attributes. We use
Py_XDECREF() here because the first and last members
could be NULL. It then calls the tp_free member of the object’s type
to free the object’s memory. Note that the object’s type might not be
NoddyType, because the object may be an instance of a subclass.

We want to make sure that the first and last names are initialized to empty
strings, so we provide a new method:

The new member is responsible for creating (as opposed to initializing) objects
of the type. It is exposed in Python as the __new__() method. See the
paper titled “Unifying types and classes in Python” for a detailed discussion of
the __new__() method. One reason to implement a new method is to assure
the initial values of instance variables. In this case, we use the new method
to make sure that the initial values of the members first and
last are not NULL. If we didn’t care whether the initial values were
NULL, we could have used PyType_GenericNew() as our new method, as we
did before. PyType_GenericNew() initializes all of the instance variable
members to NULL.

The new method is a static method that is passed the type being instantiated and
any arguments passed when the type was called, and that returns the new object
created. New methods always accept positional and keyword arguments, but they
often ignore the arguments, leaving the argument handling to initializer
methods. Note that if the type supports subclassing, the type passed may not be
the type being defined. The new method calls the tp_alloc slot to allocate
memory. We don’t fill the tp_alloc slot ourselves. Rather
PyType_Ready() fills it for us by inheriting it from our base class,
which is object by default. Most types use the default allocation.

Note

If you are creating a co-operative tp_new (one that calls a base type’s
tp_new or __new__()), you must not try to determine what method
to call using method resolution order at runtime. Always statically determine
what type you are going to call, and call its tp_new directly, or via
type->tp_base->tp_new. If you do not do this, Python subclasses of your
type that also inherit from other Python-defined classes may not work correctly.
(Specifically, you may not be able to create instances of such subclasses
without getting a TypeError.)

The tp_init slot is exposed in Python as the __init__() method. It
is used to initialize an object after it’s created. Unlike the new method, we
can’t guarantee that the initializer is called. The initializer isn’t called
when unpickling objects and it can be overridden. Our initializer accepts
arguments to provide initial values for our instance. Initializers always accept
positional and keyword arguments.

Initializers can be called multiple times. Anyone can call the __init__()
method on our objects. For this reason, we have to be extra careful when
assigning the new values. We might be tempted, for example to assign the
first member like this:

But this would be risky. Our type doesn’t restrict the type of the
first member, so it could be any kind of object. It could have a
destructor that causes code to be executed that tries to access the
first member. To be paranoid and protect ourselves against this
possibility, we almost always reassign members before decrementing their
reference counts. When don’t we have to do this?

when we absolutely know that the reference count is greater than 1

when we know that deallocation of the object [1] will not cause any calls
back into our type’s code

when decrementing a reference count in a tp_dealloc handler when
garbage-collections is not supported [2]

We want to expose our instance variables as attributes. There are a
number of ways to do that. The simplest way is to define member definitions:

Each member definition has a member name, type, offset, access flags and
documentation string. See the Generic Attribute Management section below for
details.

A disadvantage of this approach is that it doesn’t provide a way to restrict the
types of objects that can be assigned to the Python attributes. We expect the
first and last names to be strings, but any Python objects can be assigned.
Further, the attributes can be deleted, setting the C pointers to NULL. Even
though we can make sure the members are initialized to non-NULL values, the
members can be set to NULL if the attributes are deleted.

We define a single method, name(), that outputs the objects name as the
concatenation of the first and last names.

The method is implemented as a C function that takes a Noddy (or
Noddy subclass) instance as the first argument. Methods always take an
instance as the first argument. Methods often take positional and keyword
arguments as well, but in this case we don’t take any and don’t need to accept
a positional argument tuple or keyword argument dictionary. This method is
equivalent to the Python method:

defname(self):return"%s %s"%(self.first,self.last)

Note that we have to check for the possibility that our first and
last members are NULL. This is because they can be deleted, in which
case they are set to NULL. It would be better to prevent deletion of these
attributes and to restrict the attribute values to be strings. We’ll see how to
do that in the next section.

Now that we’ve defined the method, we need to create an array of method
definitions:

staticPyMethodDefNoddy_methods[]={{"name",(PyCFunction)Noddy_name,METH_NOARGS,"Return the name, combining the first and last name"},{NULL}/* Sentinel */};

Note that we used the METH_NOARGS flag to indicate that the method is
passed no arguments.

Finally, we’ll make our type usable as a base class. We’ve written our methods
carefully so far so that they don’t make any assumptions about the type of the
object being created or used, so all we need to do is to add the
Py_TPFLAGS_BASETYPE to our class flag definition:

Py_TPFLAGS_DEFAULT|Py_TPFLAGS_BASETYPE,/*tp_flags*/

We rename initnoddy() to initnoddy2() and update the module name
passed to Py_InitModule3().

In this section, we’ll provide finer control over how the first and
last attributes are set in the Noddy example. In the previous
version of our module, the instance variables first and last
could be set to non-string values or even deleted. We want to make sure that
these attributes always contain strings.

To provide greater control, over the first and last attributes,
we’ll use custom getter and setter functions. Here are the functions for
getting and setting the first attribute:

Noddy_getfirst(Noddy*self,void*closure){Py_INCREF(self->first);returnself->first;}staticintNoddy_setfirst(Noddy*self,PyObject*value,void*closure){if(value==NULL){PyErr_SetString(PyExc_TypeError,"Cannot delete the first attribute");return-1;}if(!PyString_Check(value)){PyErr_SetString(PyExc_TypeError,"The first attribute value must be a string");return-1;}Py_DECREF(self->first);Py_INCREF(value);self->first=value;return0;}

The getter function is passed a Noddy object and a “closure”, which is
void pointer. In this case, the closure is ignored. (The closure supports an
advanced usage in which definition data is passed to the getter and setter. This
could, for example, be used to allow a single set of getter and setter functions
that decide the attribute to get or set based on data in the closure.)

The setter function is passed the Noddy object, the new value, and the
closure. The new value may be NULL, in which case the attribute is being
deleted. In our setter, we raise an error if the attribute is deleted or if the
attribute value is not a string.

With these changes, we can assure that the first and last
members are never NULL so we can remove checks for NULL values in almost all
cases. This means that most of the Py_XDECREF() calls can be converted to
Py_DECREF() calls. The only place we can’t change these calls is in the
deallocator, where there is the possibility that the initialization of these
members failed in the constructor.

We also rename the module initialization function and module name in the
initialization function, as we did before, and we add an extra definition to the
setup.py file.

Python has a cyclic-garbage collector that can identify unneeded objects even
when their reference counts are not zero. This can happen when objects are
involved in cycles. For example, consider:

>>>l=[]>>>l.append(l)>>>dell

In this example, we create a list that contains itself. When we delete it, it
still has a reference from itself. Its reference count doesn’t drop to zero.
Fortunately, Python’s cyclic-garbage collector will eventually figure out that
the list is garbage and free it.

In the second version of the Noddy example, we allowed any kind of
object to be stored in the first or last attributes. [4] This
means that Noddy objects can participate in cycles:

>>>importnoddy2>>>n=noddy2.Noddy()>>>l=[n]>>>n.first=l

This is pretty silly, but it gives us an excuse to add support for the
cyclic-garbage collector to the Noddy example. To support cyclic
garbage collection, types need to fill two slots and set a class flag that
enables these slots:

For each subobject that can participate in cycles, we need to call the
visit() function, which is passed to the traversal method. The
visit() function takes as arguments the subobject and the extra argument
arg passed to the traversal method. It returns an integer value that must be
returned if it is non-zero.

Python 2.4 and higher provide a Py_VISIT() macro that automates calling
visit functions. With Py_VISIT(), Noddy_traverse() can be
simplified:

Notice the use of a temporary variable in Noddy_clear(). We use the
temporary variable so that we can set each member to NULL before decrementing
its reference count. We do this because, as was discussed earlier, if the
reference count drops to zero, we might cause code to run that calls back into
the object. In addition, because we now support garbage collection, we also
have to worry about code being run that triggers garbage collection. If garbage
collection is run, our tp_traverse handler could get called. We can’t
take a chance of having Noddy_traverse() called when a member’s reference
count has dropped to zero and its value hasn’t been set to NULL.

Python 2.4 and higher provide a Py_CLEAR() that automates the careful
decrementing of reference counts. With Py_CLEAR(), the
Noddy_clear() function can be simplified:

Note that Noddy_dealloc() may call arbitrary functions through
__del__ method or weakref callback. It means circular GC can be
triggered inside the function. Since GC assumes reference count is not zero,
we need to untrack the object from GC by calling PyObject_GC_UnTrack()
before clearing members. Here is reimplemented deallocator which uses
PyObject_GC_UnTrack() and Noddy_clear().

It is possible to create new extension types that are derived from existing
types. It is easiest to inherit from the built in types, since an extension can
easily use the PyTypeObject it needs. It can be difficult to share
these PyTypeObject structures between extension modules.

In this example we will create a Shoddy type that inherits from the
built-in list type. The new type will be completely compatible with
regular lists, but will have an additional increment() method that
increases an internal counter.

As you can see, the source code closely resembles the Noddy examples in
previous sections. We will break down the main differences between them.

typedefstruct{PyListObjectlist;intstate;}Shoddy;

The primary difference for derived type objects is that the base type’s object
structure must be the first value. The base type will already include the
PyObject_HEAD() at the beginning of its structure.

When a Python object is a Shoddy instance, its PyObject* pointer can
be safely cast to both PyListObject* and Shoddy*.

In the __init__ method for our type, we can see how to call through to
the __init__ method of the base type.

This pattern is important when writing a type with custom new and
dealloc methods. The new method should not actually create the
memory for the object with tp_alloc, that will be handled by the base
class when calling its tp_new.

When filling out the PyTypeObject() for the Shoddy type, you see
a slot for tp_base(). Due to cross platform compiler issues, you can’t
fill that field directly with the PyList_Type(); it can be done later in
the module’s init() function.

Before calling PyType_Ready(), the type structure must have the
tp_base slot filled in. When we are deriving a new type, it is not
necessary to fill out the tp_alloc slot with PyType_GenericNew()
– the allocate function from the base type will be inherited.

After that, calling PyType_Ready() and adding the type object to the
module is the same as with the basic Noddy examples.

Now that’s a lot of methods. Don’t worry too much though - if you have a type
you want to define, the chances are very good that you will only implement a
handful of these.

As you probably expect by now, we’re going to go over this and give more
information about the various handlers. We won’t go in the order they are
defined in the structure, because there is a lot of historical baggage that
impacts the ordering of the fields; be sure your type initialization keeps the
fields in the right order! It’s often easiest to find an example that includes
all the fields you need (even if they’re initialized to 0) and then change
the values to suit your new type.

char*tp_name;/* For printing */

The name of the type - as mentioned in the last section, this will appear in
various places, almost entirely for diagnostic purposes. Try to choose something
that will be helpful in such a situation!

inttp_basicsize,tp_itemsize;/* For allocation */

These fields tell the runtime how much memory to allocate when new objects of
this type are created. Python has some built-in support for variable length
structures (think: strings, lists) which is where the tp_itemsize field
comes in. This will be dealt with later.

char*tp_doc;

Here you can put a string (or its address) that you want returned when the
Python script references obj.__doc__ to retrieve the doc string.

Now we come to the basic type methods—the ones most extension types will
implement.

This function is called when the reference count of the instance of your type is
reduced to zero and the Python interpreter wants to reclaim it. If your type
has memory to free or other clean-up to perform, you can put it here. The
object itself needs to be freed here as well. Here is an example of this
function:

One important requirement of the deallocator function is that it leaves any
pending exceptions alone. This is important since deallocators are frequently
called as the interpreter unwinds the Python stack; when the stack is unwound
due to an exception (rather than normal returns), nothing is done to protect the
deallocators from seeing that an exception has already been set. Any actions
which a deallocator performs which may cause additional Python code to be
executed may detect that an exception has been set. This can lead to misleading
errors from the interpreter. The proper way to protect against this is to save
a pending exception before performing the unsafe action, and restoring it when
done. This can be done using the PyErr_Fetch() and
PyErr_Restore() functions:

In Python, there are three ways to generate a textual representation of an
object: the repr() function (or equivalent back-tick syntax), the
str() function, and the print statement. For most objects, the
print statement is equivalent to the str() function, but it is
possible to special-case printing to a FILE* if necessary; this should
only be done if efficiency is identified as a problem and profiling suggests
that creating a temporary string object to be written to a file is too
expensive.

These handlers are all optional, and most types at most need to implement the
tp_str and tp_repr handlers.

reprfunctp_repr;reprfunctp_str;printfunctp_print;

The tp_repr handler should return a string object containing a
representation of the instance for which it is called. Here is a simple
example:

If no tp_repr handler is specified, the interpreter will supply a
representation that uses the type’s tp_name and a uniquely-identifying
value for the object.

The tp_str handler is to str() what the tp_repr handler
described above is to repr(); that is, it is called when Python code calls
str() on an instance of your object. Its implementation is very similar
to the tp_repr function, but the resulting string is intended for human
consumption. If tp_str is not specified, the tp_repr handler is
used instead.

The print function will be called whenever Python needs to “print” an instance
of the type. For example, if ‘node’ is an instance of type TreeNode, then the
print function is called when Python code calls:

printnode

There is a flags argument and one flag, Py_PRINT_RAW, and it suggests
that you print without string quotes and possibly without interpreting escape
sequences.

The print function receives a file object as an argument. You will likely want
to write to that file object.

For every object which can support attributes, the corresponding type must
provide the functions that control how the attributes are resolved. There needs
to be a function which can retrieve attributes (if any are defined), and another
to set attributes (if setting attributes is allowed). Removing an attribute is
a special case, for which the new value passed to the handler is NULL.

Python supports two pairs of attribute handlers; a type that supports attributes
only needs to implement the functions for one pair. The difference is that one
pair takes the name of the attribute as a char*, while the other
accepts a PyObject*. Each type can use whichever pair makes more
sense for the implementation’s convenience.

If accessing attributes of an object is always a simple operation (this will be
explained shortly), there are generic implementations which can be used to
provide the PyObject* version of the attribute management functions.
The actual need for type-specific attribute handlers almost completely
disappeared starting with Python 2.2, though there are many examples which have
not been updated to use some of the new generic mechanism that is available.

Most extension types only use simple attributes. So, what makes the
attributes simple? There are only a couple of conditions that must be met:

The name of the attributes must be known when PyType_Ready() is
called.

No special processing is needed to record that an attribute was looked up or
set, nor do actions need to be taken based on the value.

Note that this list does not place any restrictions on the values of the
attributes, when the values are computed, or how relevant data is stored.

When PyType_Ready() is called, it uses three tables referenced by the
type object to create descriptors which are placed in the dictionary of the
type object. Each descriptor controls access to one attribute of the instance
object. Each of the tables is optional; if all three are NULL, instances of
the type will only have attributes that are inherited from their base type, and
should leave the tp_getattro and tp_setattro fields NULL as
well, allowing the base type to handle attributes.

One entry should be defined for each method provided by the type; no entries are
needed for methods inherited from a base type. One additional entry is needed
at the end; it is a sentinel that marks the end of the array. The
ml_name field of the sentinel must be NULL.

XXX Need to refer to some unified discussion of the structure fields, shared
with the next section.

The second table is used to define attributes which map directly to data stored
in the instance. A variety of primitive C types are supported, and access may
be read-only or read-write. The structures in the table are defined as:

For each entry in the table, a descriptor will be constructed and added to the
type which will be able to extract a value from the instance structure. The
type field should contain one of the type codes defined in the
structmember.h header; the value will be used to determine how to
convert Python values to and from C values. The flags field is used to
store flags which control how the attribute can be accessed.

XXX Need to move some of this to a shared section!

The following flag constants are defined in structmember.h; they may be
combined using bitwise-OR.

Constant

Meaning

READONLY

Never writable.

RO

Shorthand for READONLY.

READ_RESTRICTED

Not readable in restricted mode.

WRITE_RESTRICTED

Not writable in restricted mode.

RESTRICTED

Not readable or writable in restricted mode.

An interesting advantage of using the tp_members table to build
descriptors that are used at runtime is that any attribute defined this way can
have an associated doc string simply by providing the text in the table. An
application can use the introspection API to retrieve the descriptor from the
class object, and get the doc string using its __doc__ attribute.

As with the tp_methods table, a sentinel entry with a name value
of NULL is required.

For simplicity, only the char* version will be demonstrated here; the
type of the name parameter is the only difference between the char*
and PyObject* flavors of the interface. This example effectively does
the same thing as the generic example above, but does not use the generic
support added in Python 2.2. The value in showing this is two-fold: it
demonstrates how basic attribute management can be done in a way that is
portable to older versions of Python, and explains how the handler functions are
called, so that if you do need to extend their functionality, you’ll understand
what needs to be done.

The tp_getattr handler is called when the object requires an attribute
look-up. It is called in the same situations where the __getattr__()
method of a class would be called.

A likely way to handle this is (1) to implement a set of functions (such as
newdatatype_getSize() and newdatatype_setSize() in the example
below), (2) provide a method table listing these functions, and (3) provide a
getattr function that returns the result of a lookup in that table. The method
table uses the same structure as the tp_methods field of the type
object.

Here is an example:

staticPyMethodDefnewdatatype_methods[]={{"getSize",(PyCFunction)newdatatype_getSize,METH_VARARGS,"Return the current size."},{"setSize",(PyCFunction)newdatatype_setSize,METH_VARARGS,"Set the size."},{NULL,NULL,0,NULL}/* sentinel */};staticPyObject*newdatatype_getattr(newdatatypeobject*obj,char*name){returnPy_FindMethod(newdatatype_methods,(PyObject*)obj,name);}

The tp_setattr handler is called when the __setattr__() or
__delattr__() method of a class instance would be called. When an
attribute should be deleted, the third parameter will be NULL. Here is an
example that simply raises an exception; if this were really all you wanted, the
tp_setattr handler should be set to NULL.

The tp_compare handler is called when comparisons are needed and the
object does not implement the specific rich comparison method which matches the
requested comparison. (It is always used if defined and the
PyObject_Compare() or PyObject_Cmp() functions are used, or if
cmp() is used from Python.) It is analogous to the __cmp__() method.
This function should return -1 if obj1 is less than obj2, 0 if they
are equal, and 1 if obj1 is greater than obj2. (It was previously
allowed to return arbitrary negative or positive integers for less than and
greater than, respectively; as of Python 2.2, this is no longer allowed. In the
future, other return values may be assigned a different meaning.)

A tp_compare handler may raise an exception. In this case it should
return a negative value. The caller has to test for the exception using
PyErr_Occurred().

Python supports a variety of abstract ‘protocols;’ the specific interfaces
provided to use these interfaces are documented in Abstract Objects Layer.

A number of these abstract interfaces were defined early in the development of
the Python implementation. In particular, the number, mapping, and sequence
protocols have been part of Python since the beginning. Other protocols have
been added over time. For protocols which depend on several handler routines
from the type implementation, the older protocols have been defined as optional
blocks of handlers referenced by the type object. For newer protocols there are
additional slots in the main type object, with a flag bit being set to indicate
that the slots are present and should be checked by the interpreter. (The flag
bit does not indicate that the slot values are non-NULL. The flag may be set
to indicate the presence of a slot, but a slot may still be unfilled.)

If you wish your object to be able to act like a number, a sequence, or a
mapping object, then you place the address of a structure that implements the C
type PyNumberMethods, PySequenceMethods, or
PyMappingMethods, respectively. It is up to you to fill in this
structure with appropriate values. You can find examples of the use of each of
these in the Objects directory of the Python source distribution.

hashfunctp_hash;

This function, if you choose to provide it, should return a hash number for an
instance of your data type. Here is a moderately pointless example:

This function is called when an instance of your data type is “called”, for
example, if obj1 is an instance of your data type and the Python script
contains obj1('hello'), the tp_call handler is invoked.

This function takes three arguments:

arg1 is the instance of the data type which is the subject of the call. If
the call is obj1('hello'), then arg1 is obj1.

arg2 is a tuple containing the arguments to the call. You can use
PyArg_ParseTuple() to extract the arguments.

arg3 is a dictionary of keyword arguments that were passed. If this is
non-NULL and you support keyword arguments, use
PyArg_ParseTupleAndKeywords() to extract the arguments. If you do not
want to support keyword arguments and this is non-NULL, raise a
TypeError with a message saying that keyword arguments are not supported.

Here is a desultory example of the implementation of the call function.

These functions provide support for the iterator protocol. Any object which
wishes to support iteration over its contents (which may be generated during
iteration) must implement the tp_iter handler. Objects which are returned
by a tp_iter handler must implement both the tp_iter and tp_iternext
handlers. Both handlers take exactly one parameter, the instance for which they
are being called, and return a new reference. In the case of an error, they
should set an exception and return NULL.

For an object which represents an iterable collection, the tp_iter handler
must return an iterator object. The iterator object is responsible for
maintaining the state of the iteration. For collections which can support
multiple iterators which do not interfere with each other (as lists and tuples
do), a new iterator should be created and returned. Objects which can only be
iterated over once (usually due to side effects of iteration) should implement
this handler by returning a new reference to themselves, and should also
implement the tp_iternext handler. File objects are an example of such an
iterator.

Iterator objects should implement both handlers. The tp_iter handler should
return a new reference to the iterator (this is the same as the tp_iter
handler for objects which can only be iterated over destructively). The
tp_iternext handler should return a new reference to the next object in the
iteration if there is one. If the iteration has reached the end, it may return
NULL without setting an exception or it may set StopIteration; avoiding
the exception can yield slightly better performance. If an actual error occurs,
it should set an exception and return NULL.

One of the goals of Python’s weak-reference implementation is to allow any type
to participate in the weak reference mechanism without incurring the overhead on
those objects which do not benefit by weak referencing (such as numbers).

For an object to be weakly referencable, the extension must include a
PyObject* field in the instance structure for the use of the weak
reference mechanism; it must be initialized to NULL by the object’s
constructor. It must also set the tp_weaklistoffset field of the
corresponding type object to the offset of the field. For example, the instance
type is defined with the following structure:

Remember that you can omit most of these functions, in which case you provide
0 as a value. There are type definitions for each of the functions you must
provide. They are in object.h in the Python include directory that
comes with the source distribution of Python.

In order to learn how to implement any specific method for your new data type,
do the following: Download and unpack the Python source distribution. Go the
Objects directory, then search the C source files for tp_ plus the
function you want (for example, tp_print or tp_compare). You will find
examples of the function you want to implement.

When you need to verify that an object is an instance of the type you are
implementing, use the PyObject_TypeCheck() function. A sample of its use
might be something like the following:

if(!PyObject_TypeCheck(some_object,&MyType)){PyErr_SetString(PyExc_TypeError,"arg #1 not a mything");returnNULL;}

We relied on this in the tp_dealloc handler in this example, because our
type doesn’t support garbage collection. Even if a type supports garbage
collection, there are calls that can be made to “untrack” the object from
garbage collection, however, these calls are advanced and not covered here.

We now know that the first and last members are strings, so perhaps we could be
less careful about decrementing their reference counts, however, we accept
instances of string subclasses. Even though deallocating normal strings won’t
call back into our objects, we can’t guarantee that deallocating an instance of
a string subclass won’t call back into our objects.